Overview

Brought to you by YData

Dataset statistics

 Loan_status = 0Loan_status = 1
Number of variables1212
Number of observations474427711
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory3.1 MiB521.6 KiB
Average record size in memory69.0 B69.3 B

Variable types

 Loan_status = 0Loan_status = 1
Numeric77
Categorical55

Alerts

Loan_status = 0Loan_status = 1
loan_status has constant value "0" loan_status has constant value "1" Constant
person_emp_length has 5796 (12.2%) zeros person_emp_length has 1413 (18.3%) zeros Zeros
Alert not present in this datasetperson_home_ownership is highly imbalanced (63.1%) Imbalance

Reproduction

 Loan_status = 0Loan_status = 1
Analysis started2024-10-11 07:15:41.5372262024-10-11 07:16:23.043335
Analysis finished2024-10-11 07:16:22.4629462024-10-11 07:16:56.985962
Duration40.93 seconds33.94 seconds
Software versionydata-profiling vv4.10.0ydata-profiling vv4.10.0
Download configurationconfig.jsonconfig.json

Variables

person_age
Real number (ℝ)

 Loan_status = 0Loan_status = 1
Distinct2525
Distinct (%)0.1%0.3%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean26.96650626.867981
 Loan_status = 0Loan_status = 1
Minimum2020
Maximum4444
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:16:57.640399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum2020
5-th percentile2222
Q12323
median2625
Q32929
95-th percentile3737
Maximum4444
Range2424
Interquartile range (IQR)66

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation4.90471115.0136468
Coefficient of variation (CV)0.181881590.18660304
Kurtosis1.00820621.0433213
Mean26.96650626.867981
Median Absolute Deviation (MAD)33
Skewness1.20511841.2393327
Sum1279345207179
Variance24.05619125.136654
MonotonicityNot monotonicNot monotonic
2024-10-11T11:16:58.519985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
23 6544
13.8%
22 5865
12.4%
24 5372
11.3%
25 4153
8.8%
27 3774
8.0%
26 3175
 
6.7%
28 3059
 
6.4%
29 2724
 
5.7%
30 1939
 
4.1%
31 1588
 
3.3%
Other values (15) 9249
19.5%
ValueCountFrequency (%)
22 1070
13.9%
23 1021
13.2%
24 856
11.1%
25 713
9.2%
26 546
 
7.1%
27 508
 
6.6%
28 492
 
6.4%
29 414
 
5.4%
21 296
 
3.8%
30 283
 
3.7%
Other values (15) 1512
19.6%
ValueCountFrequency (%)
20 9
 
< 0.1%
21 1473
 
3.1%
22 5865
12.4%
23 6544
13.8%
24 5372
11.3%
25 4153
8.8%
26 3175
6.7%
27 3774
8.0%
28 3059
6.4%
29 2724
5.7%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 296
 
3.8%
22 1070
13.9%
23 1021
13.2%
24 856
11.1%
25 713
9.2%
26 546
7.1%
27 508
6.6%
28 492
6.4%
29 414
 
5.4%
ValueCountFrequency (%)
20 2
 
< 0.1%
21 296
 
0.6%
22 1070
2.3%
23 1021
2.2%
24 856
1.8%
25 713
1.5%
26 546
1.2%
27 508
1.1%
28 492
1.0%
29 414
 
0.9%
ValueCountFrequency (%)
20 9
 
0.1%
21 1473
 
19.1%
22 5865
76.1%
23 6544
84.9%
24 5372
69.7%
25 4153
53.9%
26 3175
41.2%
27 3774
48.9%
28 3059
39.7%
29 2724
35.3%

person_income
Real number (ℝ)

 Loan_status = 0Loan_status = 1
Distinct2088834
Distinct (%)4.4%10.8%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean62773.55347387.325
 Loan_status = 0Loan_status = 1
Minimum42009600
Maximum142500142800
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:16:59.290499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum42009600
5-th percentile3000024000
Q14500032000
median6000044000
Q375949.559000
95-th percentile11700083500
Maximum142500142800
Range138300133200
Interquartile range (IQR)30949.527000

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation25383.77719769.913
Coefficient of variation (CV)0.404370560.41719833
Kurtosis0.250745411.6826209
Mean62773.55347387.325
Median Absolute Deviation (MAD)1560013000
Skewness0.799141921.1266421
Sum2.9781029 × 1093.6540367 × 108
Variance6.4433613 × 1083.9084947 × 108
MonotonicityNot monotonicNot monotonic
2024-10-11T11:17:00.383227image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 3862
 
8.1%
50000 2815
 
5.9%
40000 1787
 
3.8%
70000 1770
 
3.7%
75000 1610
 
3.4%
45000 1423
 
3.0%
30000 1415
 
3.0%
80000 1401
 
3.0%
65000 1391
 
2.9%
90000 1286
 
2.7%
Other values (2078) 28682
60.5%
ValueCountFrequency (%)
30000 684
 
8.9%
54000 360
 
4.7%
44000 267
 
3.5%
36000 251
 
3.3%
34000 214
 
2.8%
39000 191
 
2.5%
24000 186
 
2.4%
60000 173
 
2.2%
42000 161
 
2.1%
40000 154
 
2.0%
Other values (824) 5070
65.8%
ValueCountFrequency (%)
4200 1
 
< 0.1%
5000 1
 
< 0.1%
9600 7
< 0.1%
10000 1
 
< 0.1%
10140 1
 
< 0.1%
12000 13
< 0.1%
12360 1
 
< 0.1%
12600 1
 
< 0.1%
12996 1
 
< 0.1%
13200 3
 
< 0.1%
ValueCountFrequency (%)
9600 6
 
0.1%
12000 20
0.3%
12500 1
 
< 0.1%
12996 1
 
< 0.1%
13000 1
 
< 0.1%
13200 2
 
< 0.1%
14000 1
 
< 0.1%
14400 29
0.4%
14500 1
 
< 0.1%
15000 17
0.2%
ValueCountFrequency (%)
9600 6
 
< 0.1%
12000 20
< 0.1%
12500 1
 
< 0.1%
12996 1
 
< 0.1%
13000 1
 
< 0.1%
13200 2
 
< 0.1%
14000 1
 
< 0.1%
14400 29
0.1%
14500 1
 
< 0.1%
15000 17
< 0.1%
ValueCountFrequency (%)
4200 1
 
< 0.1%
5000 1
 
< 0.1%
9600 7
0.1%
10000 1
 
< 0.1%
10140 1
 
< 0.1%
12000 13
0.2%
12360 1
 
< 0.1%
12600 1
 
< 0.1%
12996 1
 
< 0.1%
13200 3
 
< 0.1%
 Loan_status = 0Loan_status = 1
Distinct44
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size417.4 KiB68.2 KiB
RENT
22989 
MORTGAGE
21503 
OWN
2882 
OTHER
 
68
RENT
6287 
MORTGAGE
1370 
OWN
 
40
OTHER
 
14

Length

 Loan_status = 0Loan_status = 1
Max length88
Median length54
Mean length5.75367824.7073013
Min length33

Characters and Unicode

 Loan_status = 0Loan_status = 1
Total characters27296636298
Distinct characters1010
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Loan_status = 0Loan_status = 1
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Loan_status = 0Loan_status = 1
1st rowRENTRENT
2nd rowOWNRENT
3rd rowOWNRENT
4th rowRENTRENT
5th rowRENTRENT

Common Values

ValueCountFrequency (%)
RENT 22989
48.5%
MORTGAGE 21503
45.3%
OWN 2882
 
6.1%
OTHER 68
 
0.1%
ValueCountFrequency (%)
RENT 6287
81.5%
MORTGAGE 1370
 
17.8%
OWN 40
 
0.5%
OTHER 14
 
0.2%

Length

2024-10-11T11:17:01.079694image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Loan_status = 0

2024-10-11T11:17:01.615049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:17:02.114380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
rent 22989
48.5%
mortgage 21503
45.3%
own 2882
 
6.1%
other 68
 
0.1%
ValueCountFrequency (%)
rent 6287
81.5%
mortgage 1370
 
17.8%
own 40
 
0.5%
other 14
 
0.2%

Most occurring characters

ValueCountFrequency (%)
R 44560
16.3%
E 44560
16.3%
T 44560
16.3%
G 43006
15.8%
N 25871
9.5%
O 24453
9.0%
M 21503
7.9%
A 21503
7.9%
W 2882
 
1.1%
H 68
 
< 0.1%
ValueCountFrequency (%)
R 7671
21.1%
E 7671
21.1%
T 7671
21.1%
N 6327
17.4%
G 2740
 
7.5%
O 1424
 
3.9%
M 1370
 
3.8%
A 1370
 
3.8%
W 40
 
0.1%
H 14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 272966
100.0%
ValueCountFrequency (%)
(unknown) 36298
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 44560
16.3%
E 44560
16.3%
T 44560
16.3%
G 43006
15.8%
N 25871
9.5%
O 24453
9.0%
M 21503
7.9%
A 21503
7.9%
W 2882
 
1.1%
H 68
 
< 0.1%
ValueCountFrequency (%)
R 7671
21.1%
E 7671
21.1%
T 7671
21.1%
N 6327
17.4%
G 2740
 
7.5%
O 1424
 
3.9%
M 1370
 
3.8%
A 1370
 
3.8%
W 40
 
0.1%
H 14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 272966
100.0%
ValueCountFrequency (%)
(unknown) 36298
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 44560
16.3%
E 44560
16.3%
T 44560
16.3%
G 43006
15.8%
N 25871
9.5%
O 24453
9.0%
M 21503
7.9%
A 21503
7.9%
W 2882
 
1.1%
H 68
 
< 0.1%
ValueCountFrequency (%)
R 7671
21.1%
E 7671
21.1%
T 7671
21.1%
N 6327
17.4%
G 2740
 
7.5%
O 1424
 
3.9%
M 1370
 
3.8%
A 1370
 
3.8%
W 40
 
0.1%
H 14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 272966
100.0%
ValueCountFrequency (%)
(unknown) 36298
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 44560
16.3%
E 44560
16.3%
T 44560
16.3%
G 43006
15.8%
N 25871
9.5%
O 24453
9.0%
M 21503
7.9%
A 21503
7.9%
W 2882
 
1.1%
H 68
 
< 0.1%
ValueCountFrequency (%)
R 7671
21.1%
E 7671
21.1%
T 7671
21.1%
N 6327
17.4%
G 2740
 
7.5%
O 1424
 
3.9%
M 1370
 
3.8%
A 1370
 
3.8%
W 40
 
0.1%
H 14
 
< 0.1%

person_emp_length
Real number (ℝ)

 Loan_status = 0Loan_status = 1
Distinct1818
Distinct (%)< 0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean4.70905533.5751524
 Loan_status = 0Loan_status = 1
Minimum00
Maximum1717
Zeros57961413
Zeros (%)12.2%18.3%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:17:02.611714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum00
5-th percentile00
Q121
median43
Q375
95-th percentile1210
Maximum1717
Range1717
Interquartile range (IQR)54

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation3.63272173.325767
Coefficient of variation (CV)0.771433230.93024483
Kurtosis0.310530391.3596936
Mean4.70905533.5751524
Median Absolute Deviation (MAD)22
Skewness0.814542581.1989271
Sum22340727568
Variance13.19666711.060726
MonotonicityNot monotonicNot monotonic
2024-10-11T11:17:03.161078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 5796
12.2%
2 5606
11.8%
3 5349
11.3%
5 4984
10.5%
4 4535
9.6%
6 4180
8.8%
1 3856
8.1%
7 3669
7.7%
8 2603
5.5%
9 1925
 
4.1%
Other values (8) 4939
10.4%
ValueCountFrequency (%)
0 1413
18.3%
2 1304
16.9%
1 1024
13.3%
3 800
10.4%
4 679
8.8%
5 574
7.4%
6 531
 
6.9%
7 455
 
5.9%
8 257
 
3.3%
9 224
 
2.9%
Other values (8) 450
 
5.8%
ValueCountFrequency (%)
0 5796
12.2%
1 3856
8.1%
2 5606
11.8%
3 5349
11.3%
4 4535
9.6%
5 4984
10.5%
6 4180
8.8%
7 3669
7.7%
8 2603
5.5%
9 1925
 
4.1%
ValueCountFrequency (%)
0 1413
18.3%
1 1024
13.3%
2 1304
16.9%
3 800
10.4%
4 679
8.8%
5 574
7.4%
6 531
 
6.9%
7 455
 
5.9%
8 257
 
3.3%
9 224
 
2.9%
ValueCountFrequency (%)
0 1413
3.0%
1 1024
2.2%
2 1304
2.7%
3 800
1.7%
4 679
1.4%
5 574
1.2%
6 531
 
1.1%
7 455
 
1.0%
8 257
 
0.5%
9 224
 
0.5%
ValueCountFrequency (%)
0 5796
75.2%
1 3856
50.0%
2 5606
72.7%
3 5349
69.4%
4 4535
58.8%
5 4984
64.6%
6 4180
54.2%
7 3669
47.6%
8 2603
33.8%
9 1925
 
25.0%

loan_intent
Categorical

 Loan_status = 0Loan_status = 1
Distinct66
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size417.5 KiB68.3 KiB
EDUCATION
10533 
MEDICAL
8554 
VENTURE
8554 
PERSONAL
7976 
DEBTCONSOLIDATION
7042 
MEDICAL
1795 
DEBTCONSOLIDATION
1628 
EDUCATION
1236 
PERSONAL
1205 
HOMEIMPROVEMENT
1011 

Length

 Loan_status = 0Loan_status = 1
Max length1717
Median length1515
Mean length9.903039510.637012
Min length77

Characters and Unicode

 Loan_status = 0Loan_status = 1
Total characters46982082022
Distinct characters1717
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Loan_status = 0Loan_status = 1
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Loan_status = 0Loan_status = 1
1st rowEDUCATIONPERSONAL
2nd rowMEDICALMEDICAL
3rd rowPERSONALVENTURE
4th rowVENTUREMEDICAL
5th rowMEDICALEDUCATION

Common Values

ValueCountFrequency (%)
EDUCATION 10533
22.2%
MEDICAL 8554
18.0%
VENTURE 8554
18.0%
PERSONAL 7976
16.8%
DEBTCONSOLIDATION 7042
14.8%
HOMEIMPROVEMENT 4783
10.1%
ValueCountFrequency (%)
MEDICAL 1795
23.3%
DEBTCONSOLIDATION 1628
21.1%
EDUCATION 1236
16.0%
PERSONAL 1205
15.6%
HOMEIMPROVEMENT 1011
13.1%
VENTURE 836
10.8%

Length

2024-10-11T11:17:03.723454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Loan_status = 0

2024-10-11T11:17:04.248803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:17:04.931260image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
education 10533
22.2%
medical 8554
18.0%
venture 8554
18.0%
personal 7976
16.8%
debtconsolidation 7042
14.8%
homeimprovement 4783
10.1%
ValueCountFrequency (%)
medical 1795
23.3%
debtconsolidation 1628
21.1%
education 1236
16.0%
personal 1205
15.6%
homeimprovement 1011
13.1%
venture 836
10.8%

Most occurring characters

ValueCountFrequency (%)
E 65562
14.0%
O 49201
10.5%
N 45930
9.8%
T 37954
8.1%
I 37954
8.1%
A 34105
 
7.3%
D 33171
 
7.1%
C 26129
 
5.6%
L 23572
 
5.0%
M 22903
 
4.9%
Other values (7) 93339
19.9%
ValueCountFrequency (%)
E 10569
12.9%
O 9347
11.4%
N 7544
9.2%
I 7298
8.9%
T 6339
7.7%
D 6287
7.7%
A 5864
7.1%
M 4828
 
5.9%
C 4659
 
5.7%
L 4628
 
5.6%
Other values (7) 14659
17.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 469820
100.0%
ValueCountFrequency (%)
(unknown) 82022
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 65562
14.0%
O 49201
10.5%
N 45930
9.8%
T 37954
8.1%
I 37954
8.1%
A 34105
 
7.3%
D 33171
 
7.1%
C 26129
 
5.6%
L 23572
 
5.0%
M 22903
 
4.9%
Other values (7) 93339
19.9%
ValueCountFrequency (%)
E 10569
12.9%
O 9347
11.4%
N 7544
9.2%
I 7298
8.9%
T 6339
7.7%
D 6287
7.7%
A 5864
7.1%
M 4828
 
5.9%
C 4659
 
5.7%
L 4628
 
5.6%
Other values (7) 14659
17.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 469820
100.0%
ValueCountFrequency (%)
(unknown) 82022
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 65562
14.0%
O 49201
10.5%
N 45930
9.8%
T 37954
8.1%
I 37954
8.1%
A 34105
 
7.3%
D 33171
 
7.1%
C 26129
 
5.6%
L 23572
 
5.0%
M 22903
 
4.9%
Other values (7) 93339
19.9%
ValueCountFrequency (%)
E 10569
12.9%
O 9347
11.4%
N 7544
9.2%
I 7298
8.9%
T 6339
7.7%
D 6287
7.7%
A 5864
7.1%
M 4828
 
5.9%
C 4659
 
5.7%
L 4628
 
5.6%
Other values (7) 14659
17.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 469820
100.0%
ValueCountFrequency (%)
(unknown) 82022
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 65562
14.0%
O 49201
10.5%
N 45930
9.8%
T 37954
8.1%
I 37954
8.1%
A 34105
 
7.3%
D 33171
 
7.1%
C 26129
 
5.6%
L 23572
 
5.0%
M 22903
 
4.9%
Other values (7) 93339
19.9%
ValueCountFrequency (%)
E 10569
12.9%
O 9347
11.4%
N 7544
9.2%
I 7298
8.9%
T 6339
7.7%
D 6287
7.7%
A 5864
7.1%
M 4828
 
5.9%
C 4659
 
5.7%
L 4628
 
5.6%
Other values (7) 14659
17.9%

loan_grade
Categorical

 Loan_status = 0Loan_status = 1
Distinct77
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size417.7 KiB68.5 KiB
A
18866 
B
17243 
C
9054 
D
1890 
E
 
334
Other values (2)
 
55
D
2803 
B
1890 
C
1391 
A
926 
E
596 
Other values (2)
 
105

Length

 Loan_status = 0Loan_status = 1
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Loan_status = 0Loan_status = 1
Total characters474427711
Distinct characters77
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Loan_status = 0Loan_status = 1
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Loan_status = 0Loan_status = 1
1st rowBB
2nd rowCD
3rd rowAC
4th rowBB
5th rowAD

Common Values

ValueCountFrequency (%)
A 18866
39.8%
B 17243
36.3%
C 9054
19.1%
D 1890
 
4.0%
E 334
 
0.7%
F 51
 
0.1%
G 4
 
< 0.1%
ValueCountFrequency (%)
D 2803
36.4%
B 1890
24.5%
C 1391
18.0%
A 926
 
12.0%
E 596
 
7.7%
F 79
 
1.0%
G 26
 
0.3%

Length

2024-10-11T11:17:05.527658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Loan_status = 0

2024-10-11T11:17:06.038997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:17:07.072688image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
a 18866
39.8%
b 17243
36.3%
c 9054
19.1%
d 1890
 
4.0%
e 334
 
0.7%
f 51
 
0.1%
g 4
 
< 0.1%
ValueCountFrequency (%)
d 2803
36.4%
b 1890
24.5%
c 1391
18.0%
a 926
 
12.0%
e 596
 
7.7%
f 79
 
1.0%
g 26
 
0.3%

Most occurring characters

ValueCountFrequency (%)
A 18866
39.8%
B 17243
36.3%
C 9054
19.1%
D 1890
 
4.0%
E 334
 
0.7%
F 51
 
0.1%
G 4
 
< 0.1%
ValueCountFrequency (%)
D 2803
36.4%
B 1890
24.5%
C 1391
18.0%
A 926
 
12.0%
E 596
 
7.7%
F 79
 
1.0%
G 26
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 18866
39.8%
B 17243
36.3%
C 9054
19.1%
D 1890
 
4.0%
E 334
 
0.7%
F 51
 
0.1%
G 4
 
< 0.1%
ValueCountFrequency (%)
D 2803
36.4%
B 1890
24.5%
C 1391
18.0%
A 926
 
12.0%
E 596
 
7.7%
F 79
 
1.0%
G 26
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 18866
39.8%
B 17243
36.3%
C 9054
19.1%
D 1890
 
4.0%
E 334
 
0.7%
F 51
 
0.1%
G 4
 
< 0.1%
ValueCountFrequency (%)
D 2803
36.4%
B 1890
24.5%
C 1391
18.0%
A 926
 
12.0%
E 596
 
7.7%
F 79
 
1.0%
G 26
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 18866
39.8%
B 17243
36.3%
C 9054
19.1%
D 1890
 
4.0%
E 334
 
0.7%
F 51
 
0.1%
G 4
 
< 0.1%
ValueCountFrequency (%)
D 2803
36.4%
B 1890
24.5%
C 1391
18.0%
A 926
 
12.0%
E 596
 
7.7%
F 79
 
1.0%
G 26
 
0.3%

loan_amnt
Real number (ℝ)

 Loan_status = 0Loan_status = 1
Distinct480282
Distinct (%)1.0%3.7%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean8612.648810727.369
 Loan_status = 0Loan_status = 1
Minimum5001000
Maximum2600025850
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:17:07.813180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum5001000
5-th percentile24002500
Q150006000
median787510000
Q31160015000
95-th percentile1840024000
Maximum2600025850
Range2550024850
Interquartile range (IQR)66009000

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation4937.56226001.678
Coefficient of variation (CV)0.573291940.55947342
Kurtosis0.96972318-0.24677795
Mean8612.648810727.369
Median Absolute Deviation (MAD)28754500
Skewness1.02055890.64441315
Sum4.0860128 × 10882718745
Variance2437952036020139
MonotonicityNot monotonicNot monotonic
2024-10-11T11:17:08.695770image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 5867
 
12.4%
5000 4486
 
9.5%
6000 4112
 
8.7%
12000 3550
 
7.5%
8000 2801
 
5.9%
15000 2513
 
5.3%
4000 2083
 
4.4%
3000 2013
 
4.2%
7000 1868
 
3.9%
20000 1115
 
2.4%
Other values (470) 17034
35.9%
ValueCountFrequency (%)
10000 938
 
12.2%
15000 583
 
7.6%
12000 536
 
7.0%
5000 515
 
6.7%
20000 430
 
5.6%
6000 376
 
4.9%
8000 355
 
4.6%
25000 313
 
4.1%
4000 249
 
3.2%
3000 230
 
3.0%
Other values (272) 3186
41.3%
ValueCountFrequency (%)
500 1
 
< 0.1%
700 1
 
< 0.1%
900 1
 
< 0.1%
1000 357
0.8%
1050 2
 
< 0.1%
1075 1
 
< 0.1%
1150 1
 
< 0.1%
1200 150
0.3%
1225 2
 
< 0.1%
1250 2
 
< 0.1%
ValueCountFrequency (%)
1000 37
0.5%
1200 19
 
0.2%
1325 3
 
< 0.1%
1350 1
 
< 0.1%
1375 3
 
< 0.1%
1400 4
 
0.1%
1450 2
 
< 0.1%
1500 63
0.8%
1600 14
 
0.2%
1675 1
 
< 0.1%
ValueCountFrequency (%)
1000 37
0.1%
1200 19
 
< 0.1%
1325 3
 
< 0.1%
1350 1
 
< 0.1%
1375 3
 
< 0.1%
1400 4
 
< 0.1%
1450 2
 
< 0.1%
1500 63
0.1%
1600 14
 
< 0.1%
1675 1
 
< 0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
700 1
 
< 0.1%
900 1
 
< 0.1%
1000 357
4.6%
1050 2
 
< 0.1%
1075 1
 
< 0.1%
1150 1
 
< 0.1%
1200 150
1.9%
1225 2
 
< 0.1%
1250 2
 
< 0.1%

loan_int_rate
Real number (ℝ)

 Loan_status = 0Loan_status = 1
Distinct321297
Distinct (%)0.7%3.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean10.25025113.248717
 Loan_status = 0Loan_status = 1
Minimum5.425.42
Maximum22.1123.06
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:17:09.560346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum5.425.42
5-th percentile6.037.49
Q17.5111.14
median10.3913.85
Q312.4215.58
95-th percentile14.7417.27
Maximum22.1123.06
Range16.6917.64
Interquartile range (IQR)4.914.44

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation2.81927972.9986286
Coefficient of variation (CV)0.275044940.2263335
Kurtosis-0.77064175-0.30067457
Mean10.25025113.248717
Median Absolute Deviation (MAD)2.512.1
Skewness0.1921621-0.47771865
Sum486292.41102160.86
Variance7.94833818.9917735
MonotonicityNot monotonicNot monotonic
2024-10-11T11:17:10.392899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.51 1945
 
4.1%
10.99 1876
 
4.0%
7.88 1605
 
3.4%
7.49 1441
 
3.0%
13.49 1225
 
2.6%
11.49 1139
 
2.4%
7.9 1089
 
2.3%
5.42 1029
 
2.2%
6.03 998
 
2.1%
7.14 898
 
1.9%
Other values (311) 34197
72.1%
ValueCountFrequency (%)
14.96 246
 
3.2%
10.99 192
 
2.5%
15.62 160
 
2.1%
11.71 139
 
1.8%
16.77 138
 
1.8%
13.49 130
 
1.7%
15.65 130
 
1.7%
15.99 121
 
1.6%
7.51 109
 
1.4%
16.29 109
 
1.4%
Other values (287) 6237
80.9%
ValueCountFrequency (%)
5.42 1029
2.2%
5.43 1
 
< 0.1%
5.79 755
1.6%
5.99 521
1.1%
6 3
 
< 0.1%
6.03 998
2.1%
6.05 1
 
< 0.1%
6.17 332
 
0.7%
6.39 95
 
0.2%
6.42 1
 
< 0.1%
ValueCountFrequency (%)
5.42 20
0.3%
5.79 17
 
0.2%
5.99 20
0.3%
6.03 17
 
0.2%
6.17 14
 
0.2%
6.54 45
0.6%
6.62 49
0.6%
6.76 4
 
0.1%
6.91 30
0.4%
6.92 13
 
0.2%
ValueCountFrequency (%)
5.42 20
< 0.1%
5.79 17
 
< 0.1%
5.99 20
< 0.1%
6.03 17
 
< 0.1%
6.17 14
 
< 0.1%
6.54 45
0.1%
6.62 49
0.1%
6.76 4
 
< 0.1%
6.91 30
0.1%
6.92 13
 
< 0.1%
ValueCountFrequency (%)
5.42 1029
13.3%
5.43 1
 
< 0.1%
5.79 755
9.8%
5.99 521
6.8%
6 3
 
< 0.1%
6.03 998
12.9%
6.05 1
 
< 0.1%
6.17 332
 
4.3%
6.39 95
 
1.2%
6.42 1
 
< 0.1%

loan_percent_income
Real number (ℝ)

 Loan_status = 0Loan_status = 1
Distinct4644
Distinct (%)0.1%0.6%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.14571780.2362949
 Loan_status = 0Loan_status = 1
Minimum00.01
Maximum0.440.44
Zeros10
Zeros (%)< 0.1%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:17:11.262479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum00.01
5-th percentile0.040.06
Q10.090.14
median0.130.24
Q30.20.33
95-th percentile0.290.4
Maximum0.440.44
Range0.440.43
Interquartile range (IQR)0.110.19

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation0.0759897770.11188205
Coefficient of variation (CV)0.521485880.47348483
Kurtosis0.095865192-1.1985311
Mean0.14571780.2362949
Median Absolute Deviation (MAD)0.050.09
Skewness0.67170159-0.074744639
Sum6913.1441822.07
Variance0.00577444620.012517593
MonotonicityNot monotonicNot monotonic
2024-10-11T11:17:12.430259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.13 2867
 
6.0%
0.1 2852
 
6.0%
0.08 2420
 
5.1%
0.17 2384
 
5.0%
0.07 2382
 
5.0%
0.11 2363
 
5.0%
0.09 2286
 
4.8%
0.12 2210
 
4.7%
0.06 2137
 
4.5%
0.15 2134
 
4.5%
Other values (36) 23407
49.3%
ValueCountFrequency (%)
0.33 544
 
7.1%
0.31 324
 
4.2%
0.32 300
 
3.9%
0.17 275
 
3.6%
0.34 265
 
3.4%
0.14 243
 
3.2%
0.36 239
 
3.1%
0.4 229
 
3.0%
0.19 224
 
2.9%
0.08 218
 
2.8%
Other values (34) 4850
62.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01 93
 
0.2%
0.02 352
 
0.7%
0.03 926
 
2.0%
0.04 1414
3.0%
0.05 1820
3.8%
0.06 2137
4.5%
0.07 2382
5.0%
0.08 2420
5.1%
0.09 2286
4.8%
ValueCountFrequency (%)
0.01 4
 
0.1%
0.02 20
 
0.3%
0.03 57
 
0.7%
0.04 109
1.4%
0.05 125
1.6%
0.06 168
2.2%
0.07 134
1.7%
0.08 218
2.8%
0.09 183
2.4%
0.1 215
2.8%
ValueCountFrequency (%)
0.01 4
 
< 0.1%
0.02 20
 
< 0.1%
0.03 57
 
0.1%
0.04 109
0.2%
0.05 125
0.3%
0.06 168
0.4%
0.07 134
0.3%
0.08 218
0.5%
0.09 183
0.4%
0.1 215
0.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01 93
 
1.2%
0.02 352
 
4.6%
0.03 926
 
12.0%
0.04 1414
18.3%
0.05 1820
23.6%
0.06 2137
27.7%
0.07 2382
30.9%
0.08 2420
31.4%
0.09 2286
29.6%
 Loan_status = 0Loan_status = 1
Distinct22
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size417.2 KiB68.0 KiB
N
41678 
Y
5764 
N
5264 
Y
2447 

Length

 Loan_status = 0Loan_status = 1
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Loan_status = 0Loan_status = 1
Total characters474427711
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Loan_status = 0Loan_status = 1
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Loan_status = 0Loan_status = 1
1st rowNN
2nd rowNN
3rd rowNY
4th rowNN
5th rowNN

Common Values

ValueCountFrequency (%)
N 41678
87.9%
Y 5764
 
12.1%
ValueCountFrequency (%)
N 5264
68.3%
Y 2447
31.7%

Length

2024-10-11T11:17:13.028656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Loan_status = 0

2024-10-11T11:17:13.640065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:17:14.062347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
n 41678
87.9%
y 5764
 
12.1%
ValueCountFrequency (%)
n 5264
68.3%
y 2447
31.7%

Most occurring characters

ValueCountFrequency (%)
N 41678
87.9%
Y 5764
 
12.1%
ValueCountFrequency (%)
N 5264
68.3%
Y 2447
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 41678
87.9%
Y 5764
 
12.1%
ValueCountFrequency (%)
N 5264
68.3%
Y 2447
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 41678
87.9%
Y 5764
 
12.1%
ValueCountFrequency (%)
N 5264
68.3%
Y 2447
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 41678
87.9%
Y 5764
 
12.1%
ValueCountFrequency (%)
N 5264
68.3%
Y 2447
31.7%
 Loan_status = 0Loan_status = 1
Distinct1616
Distinct (%)< 0.1%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean5.50604955.4247179
 Loan_status = 0Loan_status = 1
Minimum22
Maximum1717
Zeros00
Zeros (%)0.0%0.0%
Negative00
Negative (%)0.0%0.0%
Memory size741.3 KiB120.5 KiB
2024-10-11T11:17:14.512647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

 Loan_status = 0Loan_status = 1
Minimum22
5-th percentile22
Q133
median44
Q388
95-th percentile1314
Maximum1717
Range1515
Interquartile range (IQR)55

Descriptive statistics

 Loan_status = 0Loan_status = 1
Standard deviation3.52837313.5753887
Coefficient of variation (CV)0.640817530.6590921
Kurtosis0.955017061.0397913
Mean5.50604955.4247179
Median Absolute Deviation (MAD)22
Skewness1.22196111.284345
Sum26121841830
Variance12.44941612.783405
MonotonicityNot monotonicNot monotonic
2024-10-11T11:17:15.448270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 8934
18.8%
2 8849
18.7%
4 8810
18.6%
9 2886
 
6.1%
8 2838
 
6.0%
7 2830
 
6.0%
6 2803
 
5.9%
10 2794
 
5.9%
5 2774
 
5.8%
14 638
 
1.3%
Other values (6) 3286
 
6.9%
ValueCountFrequency (%)
2 1528
19.8%
4 1491
19.3%
3 1485
19.3%
8 452
 
5.9%
9 439
 
5.7%
6 426
 
5.5%
10 418
 
5.4%
5 415
 
5.4%
7 401
 
5.2%
14 134
 
1.7%
Other values (6) 522
 
6.8%
ValueCountFrequency (%)
2 8849
18.7%
3 8934
18.8%
4 8810
18.6%
5 2774
 
5.8%
6 2803
 
5.9%
7 2830
 
6.0%
8 2838
 
6.0%
9 2886
 
6.1%
10 2794
 
5.9%
11 610
 
1.3%
ValueCountFrequency (%)
2 1528
19.8%
3 1485
19.3%
4 1491
19.3%
5 415
 
5.4%
6 426
 
5.5%
7 401
 
5.2%
8 452
 
5.9%
9 439
 
5.7%
10 418
 
5.4%
11 81
 
1.1%
ValueCountFrequency (%)
2 1528
3.2%
3 1485
3.1%
4 1491
3.1%
5 415
 
0.9%
6 426
 
0.9%
7 401
 
0.8%
8 452
 
1.0%
9 439
 
0.9%
10 418
 
0.9%
11 81
 
0.2%
ValueCountFrequency (%)
2 8849
114.8%
3 8934
115.9%
4 8810
114.3%
5 2774
 
36.0%
6 2803
 
36.4%
7 2830
 
36.7%
8 2838
 
36.8%
9 2886
 
37.4%
10 2794
 
36.2%
11 610
 
7.9%

loan_status
Categorical

 Loan_status = 0Loan_status = 1
Distinct11
Distinct (%)< 0.1%< 0.1%
Missing00
Missing (%)0.0%0.0%
Memory size417.1 KiB67.9 KiB
0
47442 
1
7711 

Length

 Loan_status = 0Loan_status = 1
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Loan_status = 0Loan_status = 1
Total characters474427711
Distinct characters11
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Loan_status = 0Loan_status = 1
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Loan_status = 0Loan_status = 1
1st row01
2nd row01
3rd row01
4th row01
5th row01

Common Values

ValueCountFrequency (%)
0 47442
100.0%
ValueCountFrequency (%)
1 7711
100.0%

Length

2024-10-11T11:17:15.941600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Loan_status = 0

2024-10-11T11:17:16.362879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:17:16.734127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 47442
100.0%
ValueCountFrequency (%)
1 7711
100.0%

Most occurring characters

ValueCountFrequency (%)
0 47442
100.0%
ValueCountFrequency (%)
1 7711
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 47442
100.0%
ValueCountFrequency (%)
1 7711
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 47442
100.0%
ValueCountFrequency (%)
1 7711
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47442
100.0%
ValueCountFrequency (%)
(unknown) 7711
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 47442
100.0%
ValueCountFrequency (%)
1 7711
100.0%

Interactions

Loan_status = 0

2024-10-11T11:16:14.870476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:51.025990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:43.526551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:23.949938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:47.990529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:28.536996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:52.190327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:33.209111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:56.908473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:38.276489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:01.978853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:42.277157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:08.753776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:47.111380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:15.540204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:51.592366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:44.420148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:24.579358image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:48.551902image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:29.253476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:52.746698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:34.036664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:57.496865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:38.822853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:03.200552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:42.833527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:09.342353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:47.655743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:16.262814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:52.181760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:45.030555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:25.136730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:49.191330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:30.017985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:53.490195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:35.044335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:58.452503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:39.424255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:04.298488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:43.605042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:09.959724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:48.245135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:16.896236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:52.778157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:45.776052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:25.714116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:49.810742image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:30.649405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:54.185659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:35.778824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:59.191995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:40.025656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:05.748068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:44.190432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:10.745242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:48.817517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:17.591702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:53.360546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:46.310407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:26.390566image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:50.386125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:31.257810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:54.938160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:36.489297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:00.040562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:40.589032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:06.901241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:45.033994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:12.106903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:49.350872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:18.253141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:53.953942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:46.878787image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:27.018985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:50.989528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:31.885228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:55.661641image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:37.112713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:00.690996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:41.193434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:07.550134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:45.968617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:13.352243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:49.922254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:18.912581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:54.541334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:47.420148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:27.600374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:51.576919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:32.543667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:15:56.239028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:37.652073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:01.240361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:41.724788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:08.138154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:46.509978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 0

2024-10-11T11:16:13.937293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Loan_status = 1

2024-10-11T11:16:50.464615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Missing values

Loan_status = 0

2024-10-11T11:16:19.945267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.

Loan_status = 1

2024-10-11T11:16:55.422919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.

Loan_status = 0

2024-10-11T11:16:21.555342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Loan_status = 1

2024-10-11T11:16:56.406575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Loan_status = 0

person_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status
03735000RENT0.0EDUCATIONB600011.490.17N140
12256000OWN6.0MEDICALC400013.350.07N20
22928800OWN8.0PERSONALA60008.900.21N100
33070000RENT14.0VENTUREB1200011.110.17N50
42260000RENT2.0MEDICALA60006.920.10N30
52745000RENT2.0VENTUREA90008.940.20N50
62545000MORTGAGE9.0EDUCATIONA120006.540.27N30
72120000RENT0.0PERSONALC250013.490.13Y30
83769600RENT11.0EDUCATIOND500014.840.07Y110
935110000MORTGAGE0.0DEBTCONSOLIDATIONC1500012.980.14Y60

Loan_status = 1

person_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status
112233000RENT6.0PERSONALB1000011.120.30N21
242230000RENT3.0MEDICALD500016.490.17N41
252525000RENT3.0VENTUREC350013.490.14Y31
383054000RENT0.0MEDICALB1250011.710.24N101
392232000RENT6.0EDUCATIOND800015.580.25N31
482944000MORTGAGE13.0EDUCATIONB1000011.860.23N81
532348000RENT7.0MEDICALA80008.900.17N41
552934560RENT8.0MEDICALE1000016.400.29Y81
582440000RENT1.0MEDICALB141259.910.35N31
602654000RENT1.0PERSONALA96009.320.18N41

Loan_status = 0

person_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status
586273457000MORTGAGE6.0PERSONALA80007.510.14N70
586282250000RENT2.0PERSONALA120008.320.24N30
586303530000RENT6.0MEDICALA30006.620.10N80
586313085000MORTGAGE6.0PERSONALA50007.510.06N70
586332437000RENT3.0EDUCATIONC900013.490.24Y20
586342475000RENT8.0VENTUREB400010.750.05N40
586362270000RENT6.0DEBTCONSOLIDATIONA100007.290.14N40
5863734120000MORTGAGE5.0EDUCATIOND2500015.950.21Y100
586402230000RENT2.0EDUCATIONA50008.900.17N30
586413175000MORTGAGE2.0VENTUREB1500011.110.20N50

Loan_status = 1

person_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status
586002449000RENT6.0DEBTCONSOLIDATIONB2000011.860.41N31
586082124000RENT5.0MEDICALC1000013.850.42N41
586212236000RENT5.0VENTUREE1600016.450.44N41
586232653000RENT10.0PERSONALD1080016.290.20N31
586252654000RENT4.0PERSONALD160014.960.03Y31
586292329654RENT1.0VENTUREA100006.540.34N21
586323269000RENT0.0DEBTCONSOLIDATIONB1200010.200.17N71
586352946610MORTGAGE1.0PERSONALD260017.580.05N61
586382828800RENT0.0MEDICALC1000012.730.35N81
586392344000RENT7.0EDUCATIOND680016.000.15N21

Duplicate rows

Loan_status = 0

person_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status# duplicates
Dataset does not contain duplicate rows.

Loan_status = 1

person_ageperson_incomeperson_home_ownershipperson_emp_lengthloan_intentloan_gradeloan_amntloan_int_rateloan_percent_incomecb_person_default_on_filecb_person_cred_hist_lengthloan_status# duplicates
Dataset does not contain duplicate rows.